Buenos Aires Data Q.C.

Data Check at L1 level for Beunos Aires

df_BA %>%
  ggplot(aes(date, confirmed, col = version))+
  geom_line()+
  labs(title = "Differences in Old (60% missing in CABA) vs New (Data direct from City) over time")

df_BA %>% 
  filter(date == max(date)) %>% 
  select(version, date, confirmed) %>% 
  pivot_wider(names_from = "version", 
              values_from = "confirmed") %>% 
  mutate(rel_change =round( ((new-old)/old )*100 ,2) ) 

Cumulative relative change: New Data has 17.5% more cases for Beunos Aires

Data Check at L2 level for Beunos Aires

df_l2 = df_BA_L2 %>% 
  filter(date == max(date)) %>% 
  select(version,salid2,loc, date, confirmed) %>% 
  pivot_wider(names_from = "version", 
              values_from = "confirmed") %>% 
  arrange()%>% 
  mutate(rel_change = round( ((new-old)/old )*100 ,2) ) %>% 
  arrange(desc(rel_change))
df_l2

p = df_l2%>% 
  ggplot(aes(old, new, group = salid2, text =loc))+
  geom_point()
library(plotly)
ggplotly(p)